Conceptualising thriving at work in hospitality: a LEGO® SERIOUS PLAY®-elicited study
Bibliographic record
Abstract
"Thriving at work" (TAW) is becoming an emerging topic of interest for academia and industry in an organisational context, such as hospitality, dotted by significant workforce challenges. Despite the surge of studies on TAW, little is known about the personal and contextual factors determining TAW in the context of the hospitality industry and its enablers and barriers. This perspective is important given the current pressures of hospitality industry organisations to source employees along with other long-term industry issues, including high contact interactions, intense social dynamics, and evolving demands. This study aims to investigate TAW conceptions through the dual perspective of front-line employees and managers. Drawing on LEGO® SERIOUS PLAY®-elicited focus groups, this study deepens our understanding of TAW in the hospitality industry context, contributing to positive organisational psychology and HR management literature and practice. It also aims to offer actionable insights for industry practitioners and policymakers to enhance employee wellbeing and performance and deliver solutions tailored to hospitality jobs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".